Overview

Dataset statistics

Number of variables12
Number of observations8390
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory786.7 KiB
Average record size in memory96.0 B

Variable types

Categorical1
Numeric11

Alerts

Symbol has a high cardinality: 2295 distinct values High cardinality
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 1 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
semi-variance (down) is highly correlated with VaR (95%)High correlation
VaR (95%) is highly correlated with semi-variance (down)High correlation
ESG Score is highly correlated with Environmental Pillar Score and 2 other fieldsHigh correlation
Environmental Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Social Pillar Score is highly correlated with ESG Score and 1 other fieldsHigh correlation
Governance Pillar Score is highly correlated with ESG ScoreHigh correlation
mean-return is highly correlated with VaR (95%)High correlation
semi-variance (down) is highly correlated with skew and 1 other fieldsHigh correlation
kurtosis is highly correlated with skewHigh correlation
skew is highly correlated with semi-variance (down) and 1 other fieldsHigh correlation
VaR (95%) is highly correlated with mean-return and 1 other fieldsHigh correlation
ESG Score has unique values Unique
semi-variance (down) has unique values Unique
kurtosis has unique values Unique
skew has unique values Unique
D(ESG, VaR) has 1658 (19.8%) zeros Zeros

Reproduction

Analysis started2022-09-27 07:55:23.971507
Analysis finished2022-09-27 07:55:36.822720
Duration12.85 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Symbol
Categorical

HIGH CARDINALITY

Distinct2295
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size65.7 KiB
GIII.OQ
 
7
BBY.N
 
7
AEO.N
 
7
HIBB.OQ
 
7
VSTO.N
 
7
Other values (2290)
8355 

Length

Max length8
Median length7
Mean length5.828843862
Min length3

Characters and Unicode

Total characters48904
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403 ?
Unique (%)4.8%

Sample

1st row360.AX
2nd row360.AX
3rd rowA.N
4th rowA.N
5th rowA.N

Common Values

ValueCountFrequency (%)
GIII.OQ7
 
0.1%
BBY.N7
 
0.1%
AEO.N7
 
0.1%
HIBB.OQ7
 
0.1%
VSTO.N7
 
0.1%
KMX.N7
 
0.1%
WSM.N7
 
0.1%
EXPR.N7
 
0.1%
ENS.N7
 
0.1%
LOW.N7
 
0.1%
Other values (2285)8320
99.2%

Length

2022-09-27T08:55:36.870390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
giii.oq7
 
0.1%
avav.oq7
 
0.1%
bby.n7
 
0.1%
mod.n7
 
0.1%
dds.n7
 
0.1%
dg.n7
 
0.1%
gme.n7
 
0.1%
dbi.n7
 
0.1%
casy.oq7
 
0.1%
schl.oq7
 
0.1%
Other values (2285)8320
99.2%

Most occurring characters

ValueCountFrequency (%)
.8390
17.2%
N6321
12.9%
O4575
 
9.4%
Q3554
 
7.3%
C2208
 
4.5%
A2080
 
4.3%
S1906
 
3.9%
T1817
 
3.7%
R1760
 
3.6%
M1456
 
3.0%
Other values (25)14837
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter40466
82.7%
Other Punctuation8390
 
17.2%
Lowercase Letter38
 
0.1%
Decimal Number6
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N6321
15.6%
O4575
 
11.3%
Q3554
 
8.8%
C2208
 
5.5%
A2080
 
5.1%
S1906
 
4.7%
T1817
 
4.5%
R1760
 
4.3%
M1456
 
3.6%
I1445
 
3.6%
Other values (16)13344
33.0%
Lowercase Letter
ValueCountFrequency (%)
a23
60.5%
b10
26.3%
p4
 
10.5%
q1
 
2.6%
Decimal Number
ValueCountFrequency (%)
32
33.3%
62
33.3%
02
33.3%
Other Punctuation
ValueCountFrequency (%)
.8390
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40504
82.8%
Common8400
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N6321
15.6%
O4575
 
11.3%
Q3554
 
8.8%
C2208
 
5.5%
A2080
 
5.1%
S1906
 
4.7%
T1817
 
4.5%
R1760
 
4.3%
M1456
 
3.6%
I1445
 
3.6%
Other values (20)13382
33.0%
Common
ValueCountFrequency (%)
.8390
99.9%
_4
 
< 0.1%
32
 
< 0.1%
62
 
< 0.1%
02
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII48904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.8390
17.2%
N6321
12.9%
O4575
 
9.4%
Q3554
 
7.3%
C2208
 
4.5%
A2080
 
4.3%
S1906
 
3.9%
T1817
 
3.7%
R1760
 
3.6%
M1456
 
3.0%
Other values (25)14837
30.3%

Year
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.890942
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-27T08:55:36.930403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12018
median2019
Q32020
95-th percentile2021
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.672251556
Coefficient of variation (CV)0.0008283020749
Kurtosis-1.034924958
Mean2018.890942
Median Absolute Deviation (MAD)1
Skewness-0.2705443575
Sum16938495
Variance2.796425267
MonotonicityNot monotonic
2022-09-27T08:55:36.985747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20201858
22.1%
20191570
18.7%
20211558
18.6%
20181244
14.8%
20171043
12.4%
2016985
11.7%
2022132
 
1.6%
ValueCountFrequency (%)
2016985
11.7%
20171043
12.4%
20181244
14.8%
20191570
18.7%
20201858
22.1%
20211558
18.6%
2022132
 
1.6%
ValueCountFrequency (%)
2022132
 
1.6%
20211558
18.6%
20201858
22.1%
20191570
18.7%
20181244
14.8%
20171043
12.4%
2016985
11.7%

ESG Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.1775578
Minimum3.806763196
Maximum94.44445553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-27T08:55:37.065767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.806763196
5-th percentile17.02965548
Q130.5849707
median43.18626799
Q359.03944378
95-th percentile78.481338
Maximum94.44445553
Range90.63769233
Interquartile range (IQR)28.45447307

Descriptive statistics

Standard deviation18.82265547
Coefficient of variation (CV)0.4166372947
Kurtosis-0.7115598409
Mean45.1775578
Median Absolute Deviation (MAD)14.05672394
Skewness0.2793725942
Sum379039.71
Variance354.2923588
MonotonicityNot monotonic
2022-09-27T08:55:37.146785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.081018031
 
< 0.1%
26.878664451
 
< 0.1%
15.585325931
 
< 0.1%
17.05280311
 
< 0.1%
43.183687071
 
< 0.1%
27.403196281
 
< 0.1%
28.907810271
 
< 0.1%
27.521227721
 
< 0.1%
28.881561021
 
< 0.1%
67.45375511
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
3.8067631961
< 0.1%
5.1148336641
< 0.1%
5.7764849911
< 0.1%
5.7944441271
< 0.1%
5.9749974951
< 0.1%
6.1522211931
< 0.1%
6.2216585671
< 0.1%
6.6863983551
< 0.1%
6.814182151
< 0.1%
6.8681014031
< 0.1%
ValueCountFrequency (%)
94.444455531
< 0.1%
93.539125651
< 0.1%
93.327841241
< 0.1%
92.806508361
< 0.1%
92.619923141
< 0.1%
92.315162631
< 0.1%
92.14299521
< 0.1%
91.905084061
< 0.1%
91.855033491
< 0.1%
91.476861231
< 0.1%

Environmental Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7249
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.66414699
Minimum0.027777778
Maximum97.98022505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-27T08:55:37.233804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.027777778
5-th percentile1.830951138
Q19.301939765
median25.35933463
Q352.52109455
95-th percentile81.62031074
Maximum97.98022505
Range97.95244727
Interquartile range (IQR)43.21915478

Descriptive statistics

Standard deviation26.09604144
Coefficient of variation (CV)0.7989200347
Kurtosis-0.8080771856
Mean32.66414699
Median Absolute Deviation (MAD)18.97278
Skewness0.6148309379
Sum274052.1932
Variance681.003379
MonotonicityNot monotonic
2022-09-27T08:55:37.324167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.15270351121
 
1.4%
1.74058178452
 
0.6%
1.77177177239
 
0.5%
1.60882140333
 
0.4%
17.5356921220
 
0.2%
20.3940886720
 
0.2%
37.3170731718
 
0.2%
4.94505494518
 
0.2%
1.92481884116
 
0.2%
6.30252100816
 
0.2%
Other values (7239)8037
95.8%
ValueCountFrequency (%)
0.0277777781
< 0.1%
0.0869565221
< 0.1%
0.0885935771
< 0.1%
0.0916722961
< 0.1%
0.1421464111
< 0.1%
0.1424501421
< 0.1%
0.1462971261
< 0.1%
0.1944444441
< 0.1%
0.2170138891
< 0.1%
0.2555583951
< 0.1%
ValueCountFrequency (%)
97.980225051
< 0.1%
97.697050951
< 0.1%
97.255203031
< 0.1%
97.168150591
< 0.1%
97.160908831
< 0.1%
97.022995931
< 0.1%
96.826191381
< 0.1%
96.679221371
< 0.1%
96.622060021
< 0.1%
96.542736541
< 0.1%

Social Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8352
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.89734695
Minimum0.453490413
Maximum97.83313378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-27T08:55:37.408168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.453490413
5-th percentile15.27588208
Q130.20751794
median45.20856376
Q362.31384454
95-th percentile84.58797876
Maximum97.83313378
Range97.37964337
Interquartile range (IQR)32.1063266

Descriptive statistics

Standard deviation21.13387719
Coefficient of variation (CV)0.4506412104
Kurtosis-0.7259666039
Mean46.89734695
Median Absolute Deviation (MAD)15.90164398
Skewness0.2685154
Sum393468.7409
Variance446.6407652
MonotonicityNot monotonic
2022-09-27T08:55:37.493186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.953331054
 
< 0.1%
25.10542163
 
< 0.1%
503
 
< 0.1%
25.916863393
 
< 0.1%
28.965558253
 
< 0.1%
22.472511383
 
< 0.1%
31.068697953
 
< 0.1%
23.843056713
 
< 0.1%
35.395305782
 
< 0.1%
10.93752
 
< 0.1%
Other values (8342)8361
99.7%
ValueCountFrequency (%)
0.4534904131
< 0.1%
1.2509850281
< 0.1%
1.3741856681
< 0.1%
2.0584982181
< 0.1%
2.2603485841
< 0.1%
2.4409562211
< 0.1%
2.7040716291
< 0.1%
2.7087430721
< 0.1%
2.8623239941
< 0.1%
2.8683574881
< 0.1%
ValueCountFrequency (%)
97.833133781
< 0.1%
97.693704961
< 0.1%
97.668742951
< 0.1%
97.655062681
< 0.1%
97.579012441
< 0.1%
97.394131821
< 0.1%
97.392935251
< 0.1%
97.348331381
< 0.1%
97.256723051
< 0.1%
97.234743751
< 0.1%

Governance Pillar Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8359
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.00145002
Minimum0.713528414
Maximum99.49668624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-27T08:55:37.581206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.713528414
5-th percentile16.13415441
Q136.54946887
median54.65678837
Q370.23126631
95-th percentile85.36482267
Maximum99.49668624
Range98.78315783
Interquartile range (IQR)33.68179744

Descriptive statistics

Standard deviation21.54495779
Coefficient of variation (CV)0.4064975162
Kurtosis-0.8158104855
Mean53.00145002
Median Absolute Deviation (MAD)16.75195087
Skewness-0.2135485784
Sum444682.1657
Variance464.1852061
MonotonicityNot monotonic
2022-09-27T08:55:37.668225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.430628532
 
< 0.1%
73.417235492
 
< 0.1%
25.723148152
 
< 0.1%
62.462575712
 
< 0.1%
35.748197452
 
< 0.1%
60.886359212
 
< 0.1%
70.701080772
 
< 0.1%
18.788167942
 
< 0.1%
51.315585142
 
< 0.1%
29.010814252
 
< 0.1%
Other values (8349)8370
99.8%
ValueCountFrequency (%)
0.7135284141
< 0.1%
1.218940231
< 0.1%
1.4498058791
< 0.1%
1.6215289681
< 0.1%
1.6709328781
< 0.1%
1.6936860071
< 0.1%
1.7419423241
< 0.1%
1.773748941
< 0.1%
1.9581911261
< 0.1%
2.0065415241
< 0.1%
ValueCountFrequency (%)
99.496686241
< 0.1%
98.610655441
< 0.1%
98.299313361
< 0.1%
98.14167411
< 0.1%
97.52325211
< 0.1%
97.499648791
< 0.1%
97.301438521
< 0.1%
97.028115551
< 0.1%
96.773164111
< 0.1%
96.673930581
< 0.1%

mean-return
Real number (ℝ)

HIGH CORRELATION

Distinct8385
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002510868964
Minimum-0.552090105
Maximum0.5359062215
Zeros0
Zeros (%)0.0%
Negative3485
Negative (%)41.5%
Memory size65.7 KiB
2022-09-27T08:55:37.758246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.552090105
5-th percentile-0.07413523606
Q1-0.01588608457
median0.00623143584
Q30.02532615983
95-th percentile0.06483100316
Maximum0.5359062215
Range1.087996327
Interquartile range (IQR)0.0412122444

Descriptive statistics

Standard deviation0.04782904166
Coefficient of variation (CV)19.04880038
Kurtosis13.67467632
Mean0.002510868964
Median Absolute Deviation (MAD)0.02046234336
Skewness-1.195183679
Sum21.0661906
Variance0.002287617226
MonotonicityNot monotonic
2022-09-27T08:55:37.845265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0394704742
 
< 0.1%
0.063013380052
 
< 0.1%
0.031460944612
 
< 0.1%
0.011522882332
 
< 0.1%
1.513940488 × 10-172
 
< 0.1%
-0.033986503021
 
< 0.1%
0.063247621591
 
< 0.1%
-0.0076424416341
 
< 0.1%
0.035432582881
 
< 0.1%
0.01750590221
 
< 0.1%
Other values (8375)8375
99.8%
ValueCountFrequency (%)
-0.5520901051
< 0.1%
-0.53041388231
< 0.1%
-0.49257455991
< 0.1%
-0.40940968471
< 0.1%
-0.40191581671
< 0.1%
-0.38965559381
< 0.1%
-0.34839798221
< 0.1%
-0.33464189151
< 0.1%
-0.33079360641
< 0.1%
-0.313157331
< 0.1%
ValueCountFrequency (%)
0.53590622151
< 0.1%
0.28352657881
< 0.1%
0.25580976621
< 0.1%
0.25518092261
< 0.1%
0.24944734791
< 0.1%
0.23766776791
< 0.1%
0.23212046331
< 0.1%
0.22828886541
< 0.1%
0.22062058531
< 0.1%
0.21998294721
< 0.1%

semi-variance (down)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.592106598
Minimum-13.09781826
Maximum2.361212071
Zeros0
Zeros (%)0.0%
Negative8375
Negative (%)99.8%
Memory size65.7 KiB
2022-09-27T08:55:37.930979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13.09781826
5-th percentile-6.698866661
Q1-5.518276647
median-4.669203134
Q3-3.694230073
95-th percentile-2.215815379
Maximum2.361212071
Range15.45903034
Interquartile range (IQR)1.824046574

Descriptive statistics

Standard deviation1.424582521
Coefficient of variation (CV)-0.3102241837
Kurtosis1.579000354
Mean-4.592106598
Median Absolute Deviation (MAD)0.9067105853
Skewness-0.07244649727
Sum-38527.77436
Variance2.029435358
MonotonicityNot monotonic
2022-09-27T08:55:38.013999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.5161816951
 
< 0.1%
-3.5450420651
 
< 0.1%
-1.9647126321
 
< 0.1%
-5.2953231991
 
< 0.1%
-4.4535201591
 
< 0.1%
-5.0397872361
 
< 0.1%
-2.6393909861
 
< 0.1%
-3.4630082131
 
< 0.1%
-3.6304496371
 
< 0.1%
-4.0300930941
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
-13.097818261
< 0.1%
-12.195113231
< 0.1%
-12.189322741
< 0.1%
-12.152660211
< 0.1%
-12.002135471
< 0.1%
-11.727299311
< 0.1%
-11.388230181
< 0.1%
-11.359324991
< 0.1%
-11.29995891
< 0.1%
-11.235959311
< 0.1%
ValueCountFrequency (%)
2.3612120711
< 0.1%
1.4818940651
< 0.1%
1.0348790971
< 0.1%
0.97846891921
< 0.1%
0.92752329921
< 0.1%
0.81725316511
< 0.1%
0.53115118351
< 0.1%
0.52113151621
< 0.1%
0.24274564911
< 0.1%
0.24211317881
< 0.1%

kurtosis
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.676361517
Minimum-4.771690305
Maximum10.66785002
Zeros0
Zeros (%)0.0%
Negative3710
Negative (%)44.2%
Memory size65.7 KiB
2022-09-27T08:55:38.103019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.771690305
5-th percentile-1.397095342
Q1-0.6374184458
median0.2110665923
Q31.478189565
95-th percentile4.462382098
Maximum10.66785002
Range15.43954032
Interquartile range (IQR)2.115608011

Descriptive statistics

Standard deviation1.848141859
Coefficient of variation (CV)2.732476364
Kurtosis2.280649397
Mean0.676361517
Median Absolute Deviation (MAD)0.9766243352
Skewness1.393491393
Sum5674.673128
Variance3.41562833
MonotonicityNot monotonic
2022-09-27T08:55:38.183037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.63282574091
 
< 0.1%
2.0940137831
 
< 0.1%
5.7915091421
 
< 0.1%
0.58111945141
 
< 0.1%
-1.3619041491
 
< 0.1%
-0.6689741971
 
< 0.1%
-0.82171287791
 
< 0.1%
0.37175294721
 
< 0.1%
0.16679804811
 
< 0.1%
0.27974915251
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
-4.7716903051
< 0.1%
-3.3034801381
< 0.1%
-3.2836624931
< 0.1%
-3.0102317641
< 0.1%
-2.7756234541
< 0.1%
-2.7722840671
< 0.1%
-2.7681182631
< 0.1%
-2.7533305481
< 0.1%
-2.7195872971
< 0.1%
-2.7124496211
< 0.1%
ValueCountFrequency (%)
10.667850021
< 0.1%
10.396198261
< 0.1%
10.392304821
< 0.1%
9.9911235261
< 0.1%
9.3306737851
< 0.1%
9.2727962921
< 0.1%
9.1362195551
< 0.1%
8.9692546151
< 0.1%
8.9098639571
< 0.1%
8.8672274541
< 0.1%

skew
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct8390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1355040776
Minimum-3.243905543
Maximum3.118139779
Zeros1
Zeros (%)< 0.1%
Negative4739
Negative (%)56.5%
Memory size65.7 KiB
2022-09-27T08:55:38.606132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.243905543
5-th percentile-1.63404865
Q1-0.7022596437
median-0.1395536166
Q30.4448158851
95-th percentile1.326255899
Maximum3.118139779
Range6.362045322
Interquartile range (IQR)1.147075529

Descriptive statistics

Standard deviation0.8902627537
Coefficient of variation (CV)-6.570007115
Kurtosis0.2027304549
Mean-0.1355040776
Median Absolute Deviation (MAD)0.5739383982
Skewness0.007182582218
Sum-1136.879211
Variance0.7925677707
MonotonicityNot monotonic
2022-09-27T08:55:38.687150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061141406491
 
< 0.1%
1.2082380141
 
< 0.1%
-2.224821931
 
< 0.1%
0.23024021141
 
< 0.1%
0.37951954941
 
< 0.1%
0.6718853871
 
< 0.1%
-0.40621576821
 
< 0.1%
-0.67593515961
 
< 0.1%
0.99501466761
 
< 0.1%
-0.79310068971
 
< 0.1%
Other values (8380)8380
99.9%
ValueCountFrequency (%)
-3.2439055431
< 0.1%
-3.1926246111
< 0.1%
-3.1921451011
< 0.1%
-2.957153091
< 0.1%
-2.915910351
< 0.1%
-2.84101981
< 0.1%
-2.8391570011
< 0.1%
-2.8164677981
< 0.1%
-2.8032260221
< 0.1%
-2.7941864191
< 0.1%
ValueCountFrequency (%)
3.1181397791
< 0.1%
2.9395501841
< 0.1%
2.9006242261
< 0.1%
2.8469504781
< 0.1%
2.8150819581
< 0.1%
2.7816161991
< 0.1%
2.7276243631
< 0.1%
2.6926845921
< 0.1%
2.6740595041
< 0.1%
2.5790034581
< 0.1%

VaR (95%)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8383
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5189475658
Minimum-1.792194881
Maximum0.3791338345
Zeros1
Zeros (%)< 0.1%
Negative8369
Negative (%)99.7%
Memory size65.7 KiB
2022-09-27T08:55:38.775170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.792194881
5-th percentile-0.7739480376
Q1-0.6047985464
median-0.5075446769
Q3-0.4212457737
95-th percentile-0.3104999932
Maximum0.3791338345
Range2.171328715
Interquartile range (IQR)0.1835527727

Descriptive statistics

Standard deviation0.1481418308
Coefficient of variation (CV)-0.285465894
Kurtosis2.766912075
Mean-0.5189475658
Median Absolute Deviation (MAD)0.09145573009
Skewness-0.3641674853
Sum-4353.970077
Variance0.02194600203
MonotonicityNot monotonic
2022-09-27T08:55:38.855187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.70242261973
 
< 0.1%
-0.48703640592
 
< 0.1%
-0.59943600522
 
< 0.1%
-0.65406361082
 
< 0.1%
-0.56287790582
 
< 0.1%
-0.64256952462
 
< 0.1%
-0.7554042641
 
< 0.1%
-0.40773219111
 
< 0.1%
-0.49951740221
 
< 0.1%
-0.77776290541
 
< 0.1%
Other values (8373)8373
99.8%
ValueCountFrequency (%)
-1.7921948811
< 0.1%
-1.5115957051
< 0.1%
-1.2583915891
< 0.1%
-1.2132071451
< 0.1%
-1.1864205381
< 0.1%
-1.1803674911
< 0.1%
-1.1763668211
< 0.1%
-1.1719023071
< 0.1%
-1.1610858391
< 0.1%
-1.1524574911
< 0.1%
ValueCountFrequency (%)
0.37913383451
< 0.1%
0.37122220281
< 0.1%
0.32035400111
< 0.1%
0.31195612641
< 0.1%
0.30012885531
< 0.1%
0.23821455781
< 0.1%
0.21590186151
< 0.1%
0.21017968251
< 0.1%
0.17959950191
< 0.1%
0.17089238681
< 0.1%

D(ESG, VaR)
Real number (ℝ≥0)

ZEROS

Distinct6733
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6199610107
Minimum0
Maximum3.615984026
Zeros1658
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size65.7 KiB
2022-09-27T08:55:38.939206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1832210288
median0.5860961193
Q30.9455968523
95-th percentile1.503668201
Maximum3.615984026
Range3.615984026
Interquartile range (IQR)0.7623758235

Descriptive statistics

Standard deviation0.5041363533
Coefficient of variation (CV)0.8131742878
Kurtosis0.5868149427
Mean0.6199610107
Median Absolute Deviation (MAD)0.3791559878
Skewness0.7070442648
Sum5201.47288
Variance0.2541534627
MonotonicityNot monotonic
2022-09-27T08:55:39.022224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01658
 
19.8%
0.2667051221
 
< 0.1%
1.4521762661
 
< 0.1%
0.47009787131
 
< 0.1%
0.35612572611
 
< 0.1%
1.8111316781
 
< 0.1%
1.6585595121
 
< 0.1%
0.89781311461
 
< 0.1%
0.85836894691
 
< 0.1%
1.219199331
 
< 0.1%
Other values (6723)6723
80.1%
ValueCountFrequency (%)
01658
19.8%
0.0041604315781
 
< 0.1%
0.0054466791261
 
< 0.1%
0.0071084197271
 
< 0.1%
0.0082407276281
 
< 0.1%
0.0091048229921
 
< 0.1%
0.0097217546671
 
< 0.1%
0.010175368241
 
< 0.1%
0.010648763011
 
< 0.1%
0.013433086981
 
< 0.1%
ValueCountFrequency (%)
3.6159840261
< 0.1%
3.6091086341
< 0.1%
3.3374431281
< 0.1%
3.2724667851
< 0.1%
3.0416922421
< 0.1%
2.9569141051
< 0.1%
2.839972341
< 0.1%
2.8201781171
< 0.1%
2.7251825551
< 0.1%
2.7053786081
< 0.1%

Interactions

2022-09-27T08:55:35.731480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.173437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.002954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.796371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.822196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.629376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.447026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.243204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.077889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.153128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.954307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.807498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.258456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.078971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.871388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.898213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.703393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.522043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.320221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.154906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.226145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.025323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.880513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.330472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.145986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.941405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.969229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.772506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.591059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.393237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.224921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.297161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.091338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.957531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.407489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.214001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.012420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.043245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.844521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.664074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.468253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.297938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.371177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.162354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.032547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.485507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.283016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.303986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.116261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.915539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.733090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.542270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.370954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.445194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.231369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.106564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.557522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.352273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.374200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.187277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.994555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.804105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.620287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.444971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.515209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.302384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.182901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.628870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.421288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.447216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.258293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.068969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.875122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.696305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.779045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.588226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.373400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.261919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.705888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.499305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.523129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.334310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.146986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.952138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.775322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.856062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.663242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.451418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.340936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.780904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.578324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.598146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.410327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.227004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.025156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.851838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:33.931079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.738259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.523434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.415953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.855921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.654348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.671162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.485344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.302020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.098171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.927856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.007096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.809275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.593449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:36.487970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:27.923936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:28.721356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:29.743179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:30.554359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:31.372036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.167187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:32.998871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.075111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:34.876290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-27T08:55:35.658464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-27T08:55:39.102242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-27T08:55:39.213267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-27T08:55:39.320290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-27T08:55:39.428315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-27T08:55:36.610997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-27T08:55:36.753716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(ESG, VaR)
0360.AX2020.027.0810188.91666743.01514418.8092710.032846-5.516182-0.6328260.061141-0.4142280.000000
1360.AX2021.031.4109588.45360845.97221025.7593070.025825-5.1432671.742724-0.646043-0.4222750.201732
2A.N2017.087.59506577.06065892.79237585.6679960.035512-4.9600900.4557900.720648-0.4587190.000000
3A.N2018.089.48925378.04573794.20181788.624684-0.061043-5.4146780.6103080.850044-0.5458390.630184
4A.N2019.088.33085579.33599794.50527384.398806-0.029269-4.1300510.126243-0.828252-0.6113490.499537
5A.N2020.087.57748979.95897793.59937083.2039840.045653-1.3057104.900414-1.988309-0.8042620.884134
6AA.N2016.087.18660585.83233081.59038698.2993130.026276-5.2486730.036191-0.293729-0.3985270.000000
7AA.N2017.087.27310990.33374879.47214995.671099-0.021786-6.0662460.566293-0.682118-0.4328550.393557
8AA.N2018.086.61997888.38353779.12899596.369097-0.033796-5.069203-0.703649-0.791101-0.5166790.661951
9AA.N2019.088.07882587.34716383.47424796.673931-0.039699-4.1923382.266191-1.274915-0.6066710.600200

Last rows

SymbolYearESG ScoreEnvironmental Pillar ScoreSocial Pillar ScoreGovernance Pillar Scoremean-returnsemi-variance (down)kurtosisskewVaR (95%)D(ESG, VaR)
8380ZUMZ.OQ2022.023.9722304.64646528.38228828.735048-0.110370-5.305590-0.820491-0.116846-0.5849650.964042
8381ZUO.N2020.036.2875293.08333354.97065830.1605370.017291-3.9881131.256732-0.901465-0.5671210.000000
8382ZUO.N2021.053.91878527.84870166.40737750.9857440.062855-5.960060-0.5513960.410789-0.2563451.976230
8383ZUO.N2022.049.73006127.33643960.10000547.518473-0.014564-5.322589-2.0065380.046289-0.4819621.574090
8384ZWS.N2016.022.3237633.08080831.54859133.3205350.077867-3.215518-1.0327950.420287-0.5807100.000000
8385ZWS.N2017.021.7566034.25006027.08554135.777625-0.029082-2.2349102.814027-1.311767-0.7891750.963631
8386ZWS.N2018.022.9664534.19339824.53159843.442041-0.127620-2.601868-1.053860-0.274952-0.7728730.238858
8387ZWS.N2019.042.38505947.92359436.35408243.6460980.012917-3.3398506.0092752.079474-0.6667871.036793
8388ZWS.N2020.059.56629853.79715376.73664343.977042-0.038499-2.857335-0.321493-0.129591-0.7324860.150479
8389ZWS.N2021.059.80939170.17463676.56941925.396839-0.017465-4.5546770.3711961.074856-0.5674420.840085